Computers have been developed that apply semantic network machines to perform artificial intelligence tasks, such as inferential retrieval. The semantic network machines represent information in a way that facilitates the retrieval of relevant facts relating to defined objects. A semantic network machine has data structures that permit each defined object to be represented only once in the semantic network machine. The data structures enable inferential retrieval to be performed easily. The data structures include objects (also referred to as nodes) and links between the nodes.
Semantic network machines have been used with “emotional agents” to perform tasks in the fields of artificial intelligence and “artificial life”. One commentator has described an emotional agent as having one or more of the following properties:
a) the agent acts in environments,
b) the agent has a plan of action,
c) the agent is autonomous,
d) the agent has its own memory area of the computer or accesses memory areas used by all agents,
e) the agent assumes a specific, defined task within an agent system,
f) the agent possesses the ability to learn, which may be supported through neural networks,
g) the agent has assessment mechanisms,
h) the agent exhibits dynamic adaptive behavior, and
i) the agent exhibits emotions that influence the agent's behavior and are influenced by the agent's behavior. See, “Künstliches Leben, Anspruch und Wirklichkeit” (Artificial Life: Contentions and Reality) by Werner Kinnebrock, 1996, Oldenbourg, ISBN 3486234854. A system of emotional agents is either structured hierarchically or operated with distributed control. The “emotions” of the emotional agents represent human moods that are carried along when operating in the agent system and thus influence the operation of the agent system. A conventional semantic network is described in detail by Eo-Pong Lim et al. in “Semantic Networks and Associative Databases: Two Approaches to Knowledge Representation and Reasoning,” IEEE Expert, Vol. 7, No. 4, August 1992, pages 31-40.
The above-mentioned technical literature describes behavior-based agent systems that intelligently process information and solve defined tasks with a high degree of success. Such agent systems can act in both artificial and real environments. Evolutionary development and individual learning are two ways in which these systems acquire their abilities. Conventional behavior-based agent systems simulate and describe emotions of humans and animals. Although computers and emotional agents are mentioned that are capable of exhibiting emotions, the purpose of doing so has been merely to simulate, describe and explain human and animal emotions.
When a modern computer network is operated at the limit of its computational speed, one of the remaining ways to enhance the performance of the computer network is to use the available computational resources more efficiently. Existing approaches to using computer resources more efficiently, however, seldom compensate for the increasing complexity of computer-implemented network structures. With increasing complexity of computer-implemented network structures, it becomes more difficult to access all of the information within the computer-implemented network structure. In particular, current approaches to managing computational resources efficiently are ill-suited to demand-oriented operation of computer-implemented network structures.
For example, current resource management approaches for a computer-implemented network structure with a semantic network machine do not consider the state of the semantic network machine that exists at a specific time. Thus, a computer-implemented network structure is sought whose operation is dependent on the state encountered by the semantic network machine.